scipy.stats.planck

scipy.stats.planck = <scipy.stats.distributions.planck_gen object at 0x434d150>

A discrete exponential discrete random variable.

Discrete random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:

Parameters :

x : array_like

quantiles

q : array_like

lower or upper tail probability

lamda : array_like

shape parameters

loc : array_like, optional

location parameter (default=0)

scale : array_like, optional

scale parameter (default=1)

size : int or tuple of ints, optional

shape of random variates (default computed from input arguments )

moments : str, optional

composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. (default=’mv’)

Alternatively, the object may be called (as a function) to fix the shape and :

location parameters returning a “frozen” discrete RV object: :

rv = planck(lamda, loc=0) :

• Frozen RV object with the same methods but holding the given shape and location fixed.

Notes

Planck (Discrete Exponential)

planck.pmf(k,b) = (1-exp(-b))*exp(-b*k) for k*b >= 0

Examples

>>> from scipy.stats import planck
>>> numargs = planck.numargs
>>> [ lamda ] = Replace with reasonable value * numargs
>>> rv = planck(lamda)

Display frozen pdf

>>> x = np.linspace(0, np.minimum(rv.dist.b, 3))
>>> h = plt.plot(x, rv.pdf(x))

Check accuracy of cdf and ppf

>>> prb = planck.cdf(x, lamda)
>>> h = plt.semilogy(np.abs(x - planck.ppf(prb, lamda)) + 1e-20)

Random number generation

>>> R = planck.rvs(lamda, size=100)

Methods

rvs(lamda, loc=0, size=1)	Random variates.
pmf(x, lamda, loc=0)	Probability mass function.
logpmf(x, lamda, loc=0)	Log of the probability mass function.
cdf(x, lamda, loc=0)	Cumulative density function.
logcdf(x, lamda, loc=0)	Log of the cumulative density function.
sf(x, lamda, loc=0)	Survival function (1-cdf — sometimes more accurate).
logsf(x, lamda, loc=0)	Log of the survival function.
ppf(q, lamda, loc=0)	Percent point function (inverse of cdf — percentiles).
isf(q, lamda, loc=0)	Inverse survival function (inverse of sf).
stats(lamda, loc=0, moments=’mv’)	Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(lamda, loc=0)	(Differential) entropy of the RV.
fit(data, lamda, loc=0)	Parameter estimates for generic data.
expect(func, lamda, loc=0, lb=None, ub=None, conditional=False)	Expected value of a function (of one argument) with respect to the distribution.
median(lamda, loc=0)	Median of the distribution.
mean(lamda, loc=0)	Mean of the distribution.
var(lamda, loc=0)	Variance of the distribution.
std(lamda, loc=0)	Standard deviation of the distribution.
interval(alpha, lamda, loc=0)	Endpoints of the range that contains alpha percent of the distribution